Matthew Schuster (2018) Undergraduate Research at Two-Year Community Colleges

This article includes a brief history of course-based undergraduate research and presents a new publication, STAR JOURNAL, for students in political science and other social science courses at two-year colleges so they can be recognized for their undergraduate research.

The Social Science Text and Academic Research (STAR) Journal is a peer/faculty-reviewed journal limited to students at two-year colleges.

CCURI Community College Undergraduate Research Initiative

Description of the CCURI project--that includes 44+ community college partners and associate colleges. The CCURI model of incorporating undergraduate research into community college curricula and employs a case study method of instruction in freshman coursework.

Resources for and information on territorial acknowledgement

Territorial acknowledgement is a common way to open conferences, meetings, etc. in a culturally sensitive and inclusive way. The first link is from the US department of arts and culture. The second is a protocol from Canadian First Peoples. The third is an example of a university-stated territorial acknowlegement for their campus, and resources for their educators related to working with indigenous communities.

Discovering indigenous science: Implications for science education

Authors: Gloria Snively & John Corsiglia
Abstract: Indigenous science relates to both the science knowledge of long‐resident, usually oral culture peoples, as well as the science knowledge of all peoples who as participants in culture are affected by the worldview and relativist interests of their home communities. This article explores aspects of multicultural science and pedagogy and describes a rich and well‐documented branch of indigenous science known to biologists and ecologists as traditional ecological knowledge (TEK)...

Height analysis R code taken from Bodine et al.

This is a script that analyzes the height change overnight from a group as a student-data generated project. See Bodine, Lenhart and Gross. 2014. Mathematics for the Life Sciences. Princeton University Press. and mathematicsforthelifesciences.com

Beyond Data Literacy: Reinventing Community Engagement and Empowerment in the Age of Data

We first discuss ‘data literacy’ as an emerging concept within a much longer historical narrative of literacy promotion. History sheds light on how defining and promoting literacy—who was literate and who was not—has been often entrenched with the constructs and perpetuation of power structures within societies—at odds with the notion of literacy as a necessarily empowering and enlightenment force. There is a risk that the same processes may play out in the age of data, at a speed and scope commensurable with those of the spread of data as a social phenomenon.

Data Science for Undergraduates: Opportunities and Options

Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent.

National Academies of Sciences, Engineering, and Medicine (2018). Data Science for Undergraduates: Opportunities and Options. Washington, DC: The National Academies Press. https://doi.org/10.17226/25104

Thinking with Data How to Turn Information into Insights

Many analysts are too concerned with tools and techniques for cleansing, modeling, and visualizing datasets and not concerned enough with asking the right questions. In this practical guide, data strategy consultant Max Shron shows you how to put the why before the how, through an often-overlooked set of analytical skills.

Thinking with Data helps you learn techniques for turning data into knowledge you can use. You’ll learn a framework for defining your project, including the data you want to collect, and how you intend to approach, organize, and analyze the results. You’ll also learn patterns of reasoning that will help you unveil the real problem that needs to be solved.

This app draws on a large species-level dataset with metabolic, life history, and ecological traits of most living and recently extinct mammal species. Users can select and plot traits, fit linear models to the data, and query displayed datapoints.